Predicting Retinal Diseases using Efficient Image Processing and Convolutional Neural Network (CNN)

Authors

  • Asif Mohammad Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Mahruf Zaman Utso Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Shifat Bin Habib Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Amit Kumar Das Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh

DOI:

https://doi.org/10.38032/jea.2021.04.008

Keywords:

Retinal Disease, Deep Learning, Image Processing, Neural Network, Convolutional Neural Network

Abstract

Neural networks in image processing are becoming a more crucial and integral part of machine learning as computational technology and hardware systems are advanced. Deep learning is also getting attention from the medical sector as it is a prominent process for classifying diseases.  There is a lot of research to predict retinal diseases using deep learning algorithms like Convolutional Neural Network (CNN). Still, there are not many researches for predicting diseases like CNV which stands for choroidal neovascularization, DME, which stands for Diabetic Macular Edema; and DRUSEN. In our research paper, the CNN (Convolutional Neural Networks) algorithm labeled the dataset of OCT retinal images into four types: CNV, DME, DRUSEN, and Natural Retina. We have also done several preprocessing on the images before passing these to the neural network. We have implemented different models for our algorithm where individual models have different hidden layers.  At the end of our following research, we have found that our algorithm CNN generates 93% accuracy.

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Published

27-12-2021
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How to Cite

Mohammad, A., Utso, M. Z., Habib, S. B., & Das, A. K. (2021). Predicting Retinal Diseases using Efficient Image Processing and Convolutional Neural Network (CNN). Journal of Engineering Advancements, 2(04), 221–227. https://doi.org/10.38032/jea.2021.04.008

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Research Articles